Evidence map›Paper›PMID 42848382›Full record

ArticleCurrent protocols2026

A Guide to Enzymatic Targeted Methylation Sequencing Bioinformatics Workflow: Best Practices.

Vitória Rodrigues Guimarães Alves, Sheila T Nagamatsu, Vanessa Kiyomi Ota, Cibele Edom Bandeira, Janitza L Montalvo-Ortiz, Danilo Micali, Ana Carolina Coelho Milani, Ivaldo Silva, Cristiane Seixas Duarte, Andrea Parolin Jackowski and 3 more

Abstract read
In one paragraph

Article in Current protocols, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

13 authors.

Vitória Rodrigues Guimarães AlvesPrograma de Pós-Graduação em Psiquiatria e Psicologia Médica, Universidade Federal de São Paulo, São Paulo, Brasil.
Sheila T NagamatsuVA Connecticut Healthcare System, West Haven, CT, USA.
Vanessa Kiyomi OtaLiNC, Laboratory of Integrative Neuroscience, Universidade Federal de São Paulo, São Paulo, Brasil.
Cibele Edom BandeiraLaboratory of Physiological Genomics of Mental Health (PhysioGen Lab), Instituto de Ciências Biomédicas da Universidade de São Paulo, São Paulo, Brasil.
Janitza L Montalvo-OrtizVA Connecticut Healthcare System, West Haven, CT, USA.
Danilo MicaliLiNC, Laboratory of Integrative Neuroscience, Universidade Federal de São Paulo, São Paulo, Brasil.
Ana Carolina Coelho MilaniDepartment of Psychiatry, Universidade Federal de São Paulo, São Paulo, Brasil.
Ivaldo SilvaDepartment of Gynaecology, Universidade Federal de São Paulo, São Paulo, Brasil.
Cristiane Seixas DuarteDepartment of Psychiatry, Columbia University Irving Medical Center and New York State Psychiatric Institute, New York, USA.
Andrea Parolin JackowskiPrograma de Pós-Graduação em Psiquiatria e Psicologia Médica, Universidade Federal de São Paulo, São Paulo, Brasil.
Jonathan PosnerDepartment of Psychiatry & Behavioral Sciences, Duke University, Durham, USA.
Carolina Muniz CarvalhoPrograma de Pós-Graduação em Psiquiatria e Psicologia Médica, Universidade Federal de São Paulo, São Paulo, Brasil.
Sintia Iole BelangeroDepartment of Psychiatry, Universidade Federal de São Paulo, São Paulo, Brasil.

Funding

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq; National Council for Scientific and Technological Development) 306982/2021-8Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq; National Council for Scientific and Technological Development) 427215/2018-8Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP; Research Support Foundation of the State of São Paulo) 2019/21612-0Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP; Research Support Foundation of the State of São Paulo) 2021/04819-0Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP; Research Support Foundation of the State of São Paulo) 2025/02111-1NIMH NIH HHS R01 MH121070-01A1
6 · The paper itself

Abstract

We present a best-practices protocol for targeted DNA methylation sequencing analysis that integrates both bisulfite- and enzymatic-conversion approaches using Twist Bioscience technology. Building on established tools and vendor-recommended workflows, this protocol provides a standardized, fully reproducible framework that addresses current gaps in the implementation, documentation, and downstream integration of targeted methylation sequencing analyses. The workflow incorporates widely used open-source tools across two main stages: preprocessing (adapter trimming with Trim Galore and quality control via FastQC/MultiQC) and methylation calling (alignment with BWA-meth, duplicate marking using Picard, and methylation extraction with MethylDackel). Importantly, we extend existing workflows by introducing a structured third stage for downstream statistical analysis in R, including β-value calculation, differential methylation analysis with methylKit, principal component analysis, and tiling-based regional profiling, all embedded in a unified, reproducible pipeline. A key contribution of this protocol is the integration of multi-stage quality control checkpoints, harmonized parameterization, and explicit reporting of intermediate outputs, enabling transparency and reproducibility across all analytical steps. The modular design allows tool substitution and adaptation to different experimental contexts, including enzyme-based methylation methods, while maintaining consistency in data processing and interpretation. This guide provides step-by-step instructions, troubleshooting strategies, and examples of expected outputs, along with built-in statistical routines that generate comprehensive results, including methylation matrices, correlation analyses, differentially methylated region annotations, and publication-ready visualizations. The complete workflow requires approximately 9-27 hr for 10 samples (approximately 134.2 million reads per sample) on Linux systems and assumes moderate proficiency in command-line environments and R. In summary, this protocol offers a practical, transparent, and reproducible framework that standardizes targeted DNA methylation analysis and facilitates its application across diverse biological and clinical research settings. © 2026 The Author(s). Current Protocols published by Wiley Periodicals LLC.

Indexed as

Computational BiologyDNA MethylationSequence Analysis, DNAHigh-Throughput Nucleotide SequencingSoftwareWorkflowDifferentially methylated regionsEpigenetics pipelineTwist NGS Methylation Detection workflow

Identifiers

PMID42848382
PMCPMC13648300

What OpenQuestion holds

Textmetadata
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.